Data Warehouses These slides are a modified version of the slides of the book Database System Concepts (Chapter 18), 5th Ed McGraw-Hill by

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1 Data Warehouses These slides are a modified version of the slides of the book Database System Concepts (Chapter 18), 5th Ed., McGraw-Hill, by Silberschatz, Korth and Sudarshan. Original slides are available at

2 Decision Support Systems Decision-support systems are used to make business decisions, often based on data collected by on-line transaction-processing systems. Examples of business decisions: What items to stock? What insurance premium to change? To whom to send advertisements? Examples of data used for making decisions Retail sales transaction details Customer profiles (income, age, gender, etc.) 1.2

3 OLTP systems Transaction Processing Systems - On-Line Transaction Processing Systems that records information about transactions ACID properties of transactions Organizations accumulate a vast amount of information generated by these systems Decision Support Systems - On-Line Analytical Processing Get high level information out of the detailed information stored in transaction processing systems Large databases OLAP systems Read-only operations. Periodic updates of data 1.3

4 Decision-Support Systems: Overview Data analysis tasks are simplified by specialized tools and SQL extensions Example tasks For each product category and each region, what were the total sales in the last quarter and how do they compare with the same quarter last year As above, for each product category and each customer category A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. Important for large businesses that generate data from multiple divisions, possibly at multiple sites Data may also be purchased externally Data mining seeks to discover knowledge automatically in the form of statistical rules and patterns from large databases. Statistical analysis packages (e.g., : S++) can be interfaced with databases Statistical analysis is a large field, but not covered here 1.4

5 Data Warehousing Data sources often store only current data, not historical data Corporate decision making requires a unified view of all organizational data, including historical data A data warehouse is a repository (archive) of information gathered from multiple sources, stored under a unified schema, at a single site Greatly simplifies querying, permits study of historical trends Shifts decision support query load away from transaction processing systems 1.5

6 Data Warehousing 1.6

7 Data warehouse architecture Metadati Data sources Data Warehouse Data Mart Data analysis Metadata: description of data on sources and on DW Data Mart: logical subset of the data warehouse for a data analysis 1.7

8 Another solution Metadati Data sources Data analysis Data Mart Sometimes the datawarehouse is too complex, direct representation of Data Mart 1.8

9 Design Issues When and how to gather data Source driven architecture: data sources transmit new information to warehouse, either continuously or periodically (e.g., at night) Destination driven architecture: warehouse periodically requests new information from data sources Keeping warehouse exactly synchronized with data sources (e.g., using two-phase commit) is too expensive Usually OK to have slightly out-of-date data at warehouse Data/updates are periodically downloaded form online transaction processing (OLTP) systems. What schema to use Schema integration 1.9

10 More Warehouse Design Issues Data cleaning E.g., correct mistakes in addresses (misspellings, zip code errors) Merge address lists from different sources and purge duplicates How to propagate updates Warehouse schema may be a (materialized) view of schema from data sources What data to summarize Raw data may be too large to store on-line Aggregate values (totals/subtotals) often suffice Queries on raw data can often be transformed by query optimizer to use aggregate values 1.10

11 Multidimensional data Consider an application where a shop wants to find out what kinds of clothes are popular. Let us suppose that clothes are characterized by item_name, colour, size and number. We have the relation sales with the schema: sales(item_name, colour, size, number) Assume item_name can take on values (skirt, dress, shirt, pant) colour (dark, pastel, white) size (small, medium, large) We can identify some of the attributes as measure attributes, since they measure some value, and can be aggregated upon. Some other attributes of the relation are identified as dimension attributes, since they define the dimension on which measure attributes Possible Dimensions on which measures are viewed for this application: - item information - time - sales location - customer information 1.11

12 Multidimensional data Data that can be modeled as dimension attributes and measure attributes are called multidimensional data. Measure attributes measure some value can be aggregated upon e.g. the attribute number of the sales relation Dimension attributes define the dimensions on which measure attributes (or aggregates thereof) are viewed e.g. the attributes item_name, color, and size of the sales relation are dimension attributes for dimension item_info 1.12

13 Warehouse Schemas Dimension values are usually encoded using small integers and mapped to full values via dimension tables Resultant schema is called a Star schema More complicated schema structures Snowflake schema: multiple levels of dimension tables Constellation: multiple fact tables 1.13

14 Data Warehouse Schema Star schema fact table dimension table fact table : BCNF dimension tables: redundant data 1.14

15 Cross Tabulation of sales by item-name and color A manager may want to see data laid out as number of sales for different combinations of item_info and colour Size: all indicates that the displayed values are a summary across all values of size. The table above is an example of a cross-tabulation (cross-tab) Values for one of the dimension attributes form the row headers Values for another dimension attribute form the column headers Other dimension attributes are listed on top Values in individual cells are (aggregates of) the values of the dimension attributes that specify the cell. In the example, the aggregation used is the sum of the values for attribute number across all size Extra column (row) storing the totals of the cells in the row (column) 1.15

16 Relational Representation of Cross-tabs Cross-tabs can be represented as relations We use the value all is used to represent aggregates (summary rows or columns) The SQL:1999 standard actually uses null values in place of all despite confusion with regular null values tuples with value all for colour and size sum (number) from sales group by item_name tuples with value all for item_name, colour and size sum (number) from sales 1.16

17 Data Cube A data cube is a multidimensional generalization of a cross-tab (which is twodimensional) Can have n dimensions; we show 3 below (time_name, colour, size) Measure attribute is number Cross-tabs can be used as views on a data cube data cube on sales relation - each cell identified by values of the three dimensions (pant, dark, medium) - each cell contains a value shown on one of the faces (pant, dark, all) summary of all values on that dimension 1.17

18 Online Analytical Processing An OLAP system is an interactive system that permits an analyst to view different summaries of multidimensional data Online indicates that an analyst must be able to request new summaries and get responses on line, and should not be forced to wait for a long time. An analyst can look at different cross-tabs on the same data by selecting the attributes in the cross-tab. Each cross-tab is a two-dimensional view of a multidimensional data cube - Pivoting - Slicing - Rollup - Drill down 1.18

19 Online Analytical Processing Pivoting: the operation of changing the dimensions used in a cross-tab 1.19

20 Online Analytical Processing Slicing: creating a cross-tab for fixed values only cross-tab for item_name and colour for a fixed value of size size cross-tab for colour and size for a fixed value of item_name item_name cross-tab for item_name and size for a fixed value of colour colour 1.20

21 Online Analytical Processing Dicing : slicing, when values for multiple dimensions are fixed. colour item_name size subset of item_names subset of colours subset of size 1.21

22 Online Analytical Processing OLAP systems permit users to view data at any desired level of granularity Rollup: moving from finer-granularity data to a coarser granularity by means of aggregation Starting from the data cube on the sales table, we got cross-tab by rolling up on size colour item_name size Drill down: The opposite operation - that of moving from coarsergranularity data to finer-granularity data Finer granularity cannot be generated by coarser granularity data; Must be generated either from the original data or from ever finer granularity summary data 1.22

23 Hierarchies on Dimensions Analysts may wish to view a dimension at different levels of details Hierarchy on dimension attributes: lets dimensions to be viewed at different levels of detail E.g. the dimension DateTime can be used to aggregate by hour of day, date, day of week, month, quarter or year 1.23

24 Cross Tabulation With Hierarchy on item-name Cross-tabs can be easily extended to deal with hierarchies Can drill down or roll up on a hierarchy Assume Clothes are grouped by category (menswear and womenswear): category lies above item_name Different levels are shown in the same cross-tab 1.24

25 OLAP Implementation The earliest OLAP systems used multidimensional arrays in memory to store data cubes, and are referred to as multidimensional OLAP (MOLAP) systems. OLAP implementations using only relational database features are called relational OLAP (ROLAP) systems Hybrid systems, which store some summaries in memory and store the base data and other summaries in a relational database, are called hybrid OLAP (HOLAP) systems. 1.25

26 OLAP Implementation (Cont.) Early OLAP systems precomputed all possible aggregates in order to provide online response Space and time requirements for doing so can be very high 2 n combinations of group by It suffices to precompute some aggregates, and compute others on demand from one of the precomputed aggregates Can compute aggregate on (item-name, color) from an aggregate on (item-name, color, size) is cheaper than computing it from scratch Several optimizations available for computing multiple aggregates Can compute aggregates on (item-name, color, size), (item-name, color) and (item-name) using a single sorting of the base data 1.26

27 An example Luogo (negozio) Roma-1 Milano-2 Milano-1 Roma-2 Basi di dati: Architetture e linee di evoluzione Atzeni, Ceri, Fraternali, Paraboschi, Torlone, Mc GrawHill Lettori DVD Quantità Televisori Lettori CD Articolo (prodotto) Tempo (trimestre) 1 trim trim trim trim Videoregistratori Fatto = vendita Misura = quantità Dimensione = Tempo, Articolo, Luogo 1.27

28 Dimensioni e gerarchie di livelli regione anno provincia categoria marca trimestre città negozio Luogo prodotto Articolo mese giorno Tempo Basi di dati: Architetture e linee di evoluzione Atzeni, Ceri, Fraternali, Paraboschi, Torlone, Mc GrawHill 1.28

29 Tempo CodiceTempo Giorno Mese Trimestre Anno Luogo CodiceLuogo Negozio Indirizzo Città Provincia Regione Vendite CodiceTempo CodiceLuogo CodiceArticolo CodiceCliente Quantità Incasso Articolo CodiceArticolo Descrizione Marca CodiceCategoria Categoria Cliente CodiceCliente Nome Cognome Sesso Età Professione Schema a stella 1.29 Basi di dati: Architetture e linee di evoluzione Atzeni, Ceri, Fraternali, Paraboschi, Torlone, Mc GrawHill

30 Basi di dati: Architetture e linee di evoluzione Atzeni, Ceri, Fraternali, Paraboschi, Torlone, Mc GrawHill 1.30

31 Extended Aggregation in SQL:1999 The cube operation computes union of group by s on every subset of the specified attributes E.g. consider the query select item-name, color, size, sum(number) from sales group by cube(item-name, color, size) This computes the union of eight different groupings of the sales relation: { (item-name, color, size), (item-name, color), (item-name, size), (color, size), (item-name), (color), (size), ( ) } where ( ) denotes an empty group by list. For each grouping, the result contains the null value for attributes not present in the grouping. 1.31

32 Extended Aggregation (Cont.) Relational representation of cross-tab that we saw earlier, but with null in place of all, can be computed by select item-name, color, sum(number) from sales group by cube(item-name, color) The function grouping() can be applied on an attribute Returns 1 if the value is a null value representing all, and returns 0 in all other cases. select item-name, color, size, sum(number), grouping(item-name) as item-name-flag, grouping(color) as color-flag, grouping(size) as size-flag, from sales group by cube(item-name, color, size) Can use the function decode() in the select clause to replace such nulls by a value such as all E.g. replace item-name in first query by decode( grouping(item-name), 1, all, item-name) 1.32

33 Extended Aggregation (Cont.) The rollup construct generates union on every prefix of specified list of attributes E.g. select item-name, color, size, sum(number) from sales group by rollup(item-name, color, size) Generates union of four groupings: { (item-name, color, size), (item-name, color), (item-name), ( ) } Rollup can be used to generate aggregates at multiple levels of a hierarchy. E.g., suppose table itemcategory(item-name, category) gives the category of each item. Then select category, item-name, sum(number) from sales, itemcategory where sales.item-name = itemcategory.item-name group by rollup(category, item-name) would give a hierarchical summary by item-name and by category. 1.33

34 Extended Aggregation (Cont.) Multiple rollups and cubes can be used in a single group by clause E.g., Each generates set of group by lists, cross product of sets gives overall set of group by lists select item-name, color, size, sum(number) from sales group by rollup(item-name), rollup(color, size) generates the groupings {item-name, ()} X {(color, size), (color), ()} = { (item-name, color, size), (item-name, color), (item-name), (color, size), (color), ( ) } 1.34

35 Ranking Ranking is done in conjunction with an order by specification. Given a relation student-marks(student-id, marks) find the rank of each student. select student-id, rank( ) over (order by marks desc) as s-rank from student-marks An extra order by clause is needed to get them in sorted order select student-id, rank ( ) over (order by marks desc) as s-rank from student-marks order by s-rank Ranking may leave gaps: e.g. if 2 students have the same top mark, both have rank 1, and the next rank is 3 dense_rank does not leave gaps, so next dense rank would be

36 Ranking (Cont.) Ranking can be done within partition of the data. Find the rank of students within each section. select student-id, section, rank ( ) over (partition by section order by marks desc) as sec-rank from student-marks, student-section where student-marks.student-id = student-section.student-id order by section, sec-rank Multiple rank clauses can occur in a single select clause Ranking is done after applying group by clause/aggregation 1.36

37 Ranking (Cont.) Other ranking functions: percent_rank (within partition, if partitioning is done) cume_dist (cumulative distribution) fraction of tuples with preceding values row_number (non-deterministic in presence of duplicates) SQL:1999 permits the user to specify nulls first or nulls last select student-id, rank ( ) over (order by marks desc nulls last) as s-rank from student-marks 1.37

38 Ranking (Cont.) For a given constant n, the ranking the function ntile(n) takes the tuples in each partition in the specified order, and divides them into n buckets with equal numbers of tuples. E.g.: select threetile, sum(salary) from ( select salary, ntile(3) over (order by salary) as threetile from employee) as s group by threetile 1.38

39 Windowing Used to smooth out random variations. E.g.: moving average: Given sales values for each date, calculate for each date the average of the sales on that day, the previous day, and the next day Window specification in SQL: Given relation sales(date, value) select date, sum(value) over (order by date between rows 1 preceding and 1 following) from sales Examples of other window specifications: between rows unbounded preceding and current rows unbounded preceding range between 10 preceding and current row All rows with values between current row value 10 to current value range interval 10 day preceding Not including current row 1.39

40 Windowing (Cont.) Can do windowing within partitions E.g. Given a relation transaction (account-number, date-time, value), where value is positive for a deposit and negative for a withdrawal Find total balance of each account after each transaction on the account select account-number, date-time, sum (value ) over (partition by account-number order by date-time rows unbounded preceding) as balance from transaction order by account-number, date-time 1.40